A neural network applied to estimate Burr XII distribution parameters

Abbasi, B., Hosseinifard, S. Z. and Coit, D. W. 2010, A neural network applied to estimate Burr XII distribution parameters, Reliability engineering and system safety, vol. 95, no. 6, pp. 647-654, doi: 10.1016/j.ress.2010.02.001.

Attached Files
Name Description MIMEType Size Downloads

Title A neural network applied to estimate Burr XII distribution parameters
Author(s) Abbasi, B.
Hosseinifard, S. Z.ORCID iD for Hosseinifard, S. Z. orcid.org/0000-0002-1064-9079
Coit, D. W.
Journal name Reliability engineering and system safety
Volume number 95
Issue number 6
Start page 647
End page 654
Total pages 8
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2010-06
ISSN 0951-8320
Keyword(s) Science & Technology
Engineering, Industrial
Operations Research & Management Science
Burr XII distribution
Artificial neural network
Parameter estimation
Summary The Burr XII distribution can closely approximate many other well-known probability density functions such as the normal, gamma, lognormal, exponential distributions as well as Pearson type I, II, V, VII, IX, X, XII families of distributions. Considering a wide range of shape and scale parameters of the Burr XII distribution, it can have an important role in reliability modeling, risk analysis and process capability estimation. However, estimating parameters of the Burr XII distribution can be a complicated task and the use of conventional methods such as maximum likelihood estimation (MLE) and moment method (MM) is not straightforward. Some tables to estimate Burr XII parameters have been provided by Burr (1942) [1] but they are not adequate for many purposes or data sets. Burr tables contain specific values of skewness and kurtosis and their corresponding Burr XII parameters. Using interpolation or extrapolation to estimate other values may provide inappropriate estimations. In this paper, we present a neural network to estimate Burr XII parameters for different values of skewness and kurtosis as inputs. A trained network is presented, and one can use it without previous knowledge about neural networks to estimate Burr XII distribution parameters. Accurate estimation of the Burr parameters is an extension of simulation studies.
Language eng
DOI 10.1016/j.ress.2010.02.001
Field of Research 150302 Business Information Systems
010401 Applied Statistics
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2010, Elsevier Ltd.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084894

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 13 times in TR Web of Science
Scopus Citation Count Cited 15 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 185 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Wed, 13 Jul 2016, 16:29:36 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.